mirror of https://github.com/alibaba/MNN.git
				
				
				
			
		
			
				
	
	
		
			66 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			66 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			C++
		
	
	
	
| //
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| //  ShapeSpaceToBatchND.cpp
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| //  MNN
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| //
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| //  Created by MNN on 2019/01/10.
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| //  Copyright © 2018, Alibaba Group Holding Limited
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| //
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| 
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| #include "shape/SizeComputer.hpp"
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| #include "core/TensorUtils.hpp"
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| 
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| namespace MNN {
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| class SpaceToBatchNDSizeComputer : public SizeComputer {
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| public:
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|     virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
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|                                const std::vector<Tensor*>& outputs) const override {
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|         MNN_ASSERT(outputs.size() == 1);
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|         MNN_ASSERT(inputs.size() == 1 || inputs.size() == 3);
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|         auto input  = inputs[0];
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|         auto output = outputs[0];
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| 
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|         int blockSize = 0;
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|         const int *blockData, *paddingData;
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|         if (inputs.size() == 3) {
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|             blockSize = inputs[1]->length(0);
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|             blockData = inputs[1]->host<int32_t>();
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|             paddingData = inputs[2]->host<int32_t>();
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|         } else {
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|             auto paramter         = op->main_as_SpaceBatch();
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|             const auto blockShape = paramter->blockShape();
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|             const auto paddings    = paramter->padding();
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|             blockSize = blockShape->dims()->data()[0];
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|             blockData = blockShape->int32s()->data();
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|             paddingData = paddings->int32s()->data();
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|         }
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|         int batch             = input->batch();
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|         for (int i = 0; i < blockSize; ++i) {
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|             batch *= blockData[i];
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|         }
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|         auto format = TensorUtils::getDescribe(input)->dimensionFormat;
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|         output->buffer().type = input->buffer().type;
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|         output->buffer().dimensions = input->buffer().dimensions;
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|         output->setLength(0, batch);
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|         TensorUtils::getDescribe(output)->dimensionFormat = format;
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|         if (MNN_DATA_FORMAT_NHWC != format) {
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|             output->setLength(1, input->length(1));
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|             for (int i = 0; i < blockSize; ++i) {
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|                 int paddedLength = input->length(2+i) + paddingData[2 * i] + paddingData[2 * i+1];
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|                 int outputLength = paddedLength / blockData[i];
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|                 output->setLength(i+2, outputLength);
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|             }
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|         } else {
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|             for (int i = 0; i < blockSize; ++i) {
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|                 int paddedLength = input->length(1 + i) + paddingData[2 * i] + paddingData[2 * i+1];
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|                 int outputLength = paddedLength / blockData[i];
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|                 output->setLength(i+1, outputLength);
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|             }
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|             output->setLength(1+blockSize, input->length(1+blockSize));
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|         }
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|         return true;
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|     }
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| };
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| 
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| REGISTER_SHAPE_INPUTS(SpaceToBatchNDSizeComputer, OpType_SpaceToBatchND, std::vector<int>({1, 2}));
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| } // namespace MNN
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